Edit model card

DISTILBERT RUNNING ON DEEPSPARSE GOES BRHMMMMMMMM. πŸš€πŸš€πŸš€

This model is πŸ‘‡

    β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•— β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•—   β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•—  β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•—  β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•— β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•—
    β–ˆβ–ˆβ•”β•β•β•β•β• β–ˆβ–ˆβ•”β•β•β–ˆβ–ˆβ•— β–ˆβ–ˆβ•”β•β•β–ˆβ–ˆβ•— β–ˆβ–ˆβ•”β•β•β–ˆβ–ˆβ•—  β–ˆβ–ˆβ•”β•β•β•β•β• β–ˆβ–ˆβ•”β•β•β•β•β•
    β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•— β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•”β• β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•‘ β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•”β•  β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•— β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•—  
    β•šβ•β•β•β•β–ˆβ–ˆβ•‘ β–ˆβ–ˆβ•”β•β•β•β•  β–ˆβ–ˆβ•”β•β•β–ˆβ–ˆβ•‘ β–ˆβ–ˆβ•”β•β•β–ˆβ–ˆβ•— β•šβ•β•β•β•β–ˆβ–ˆβ•‘β–ˆ β–ˆβ•”β•β•β•  
    β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•‘ β–ˆβ–ˆβ•‘      β–ˆβ–ˆβ•‘  β–ˆβ–ˆβ•‘ β–ˆβ–ˆβ•‘  β–ˆβ–ˆ β•‘β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•‘ β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•—
    β•šβ•β•β•β•β•β•β• β•šβ•β•      β•šβ•β•  β•šβ•β• β•šβ•β•  β•šβ• β•β•šβ•β•β•β•β•β•β• β•šβ•β•β•β•β•β•β•
                                                                                                     

Alt Text

LOOKS LIKE THIS πŸ‘‡

Imgur

Inference endpoints, outside of outliers (4ms) is avg. latency on 2 vCPUs:

Imgur

Handler for access to inference endpoints

class EndpointHandler:

    def __init__(self, path=""):

        self.pipeline = Pipeline.create(task="text-classification", model_path=path)

    def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
        """
        Args:
            data (:obj:): prediction input text
        """
        inputs = data.pop("inputs", data)

        start = perf_counter()
        prediction = self.pipeline(inputs)
        end = perf_counter()
        latency = end - start

        return {
            "labels": prediction.labels, 
            "scores": prediction.scores,
            "latency (secs.)": latency
        }

̷͈̍ Μ΅Ν’Μ³RΜΆΝƒΜ™i̸̟͘cΜ΄Μ†Μ»kΜΈΜ‘ΝœyΜ·Ν„Μ³ ̸̚Μͺ Μ·Ν€Ν–

Downloads last month
1